Image Texture Segmentation Using Polar S-Transform and Principal Component Analysis

1. A method for segmenting texture of multi-dimensional data indicative of a characteristic of an object comprising: receiving the multi-dimensional data; transforming the multi-dimensional data into second multi-dimensional data within a Stockwell domain using a rotation-invariant form of the S-transform of the multi-dimensional data; applying principal component analysis to the second multi-dimensional data for generating texture data characterizing texture around data points of at least a portion of the multi-dimensional data; and, partitioning the data points of the at least a portion of the multi-dimensional data into clusters based on the texture data using a classification process.

2. A method for segmenting texture as defined in claim 1 wherein the rotation-invariant form of the S-transform is a polar S-transform.

3. A method for segmenting texture as defined in claim 2 comprising: producing a texture map based on the partitioned data points of the multi-dimensional data, wherein data points of the multi-dimensional data within a cluster corresponding to a same texture region have a same texture value assigned thereto.

4. A method for segmenting texture as defined in claim 3 wherein the texture map is produced based on a probability of each partitioned data point of belonging to at least one of the clusters.

5. A method for segmenting texture as defined in claim 3 comprising: superimposing the texture map to the multi-dimensional data such that texture values and data values of respective data points of the texture map and the multi-dimensional data are superimposed.

6. A method for segmenting texture as defined in claim 2 comprising: determining a modified Stockwell spectrum by integrating local spectra along a radial direction.

7. A method for segmenting texture as defined in claim 6 comprising: transforming the multi-dimensional data into a Fourier domain.

8. A method for segmenting texture as defined in claim 7 wherein transforming the multi-dimensional data comprises performing for each local spectrum corresponding to a data point of the multi-dimensional data: calculating a current center frequency and a corresponding orientation angle; calculating a localizing Gaussian window at the current centre frequency; shifting the Fourier transformed multi-dimensional data by frequency components corresponding to the current centre frequency; producing product data by pointwise multiplying the shifted Fourier transformed multi-dimensional data with the localizing Gaussian window; inverse Fourier Transforming the product data; and, updating the modified Stockwell spectrum based on the inverse Fourier transformed product data.

9. A method for segmenting texture as defined in claim 8 wherein a mean of the multi-dimensional data is assigned to a respective data point in the modified Stockwell spectrum if the centre frequency corresponding to the data point is zero.

10. A method for segmenting texture as defined in claim 6 wherein applying principal component analysis comprises projecting the second multi-dimensional data onto principal components.

11. A method for segmenting texture as defined in claim 10 wherein the principal component analysis is applied along a central frequency axis of the modified Stockwell spectrum.

12. A method for segmenting texture as defined in claim 11 wherein a number of significant principal components is determined based on an accumulate sum of corresponding eigenvalues of the modified Stockwell spectrum.

13. A method for segmenting texture as defined in claim 3 wherein the multi-dimensional data are MR image data.

14. A method for segmenting texture as defined in claim 13 wherein the data points of the at least a portion of the multi-dimensional data are partitioned into data points corresponding to image pixels representing normal appearing white matter and data points corresponding to image pixels representing non normal appearing white matter.

15. A storage medium having stored therein executable commands for execution on a processor, the processor when executing the commands performing: receiving the multi-dimensional data; transforming the multi-dimensional data into second multi-dimensional data within a Stockwell domain using a polar S-transform of the multi-dimensional data; applying principal component analysis to the second multi-dimensional data for generating texture data characterizing texture around each data point of at least a portion of the multi-dimensional data; and, partitioning the data points of the at least a portion of the multi-dimensional data into clusters based on the texture data using a classification process.

16. A storage medium as defined in claim 15 having stored therein executable commands for execution on a processor, the processor when executing the commands performing: producing a texture map based on the partitioned data points of the multi-dimensional data, wherein data points of the multi-dimensional data within a cluster corresponding to a same texture region have a same texture value assigned thereto.

17. A storage medium as defined in claim 16 having stored therein executable commands for execution on a processor, the processor when executing the commands performing: superimposing the texture map to the multi-dimensional data such that texture values and data values of respective data points of the texture map and the multi-dimensional data are superimposed.

18. A storage medium as defined in claim 17 having stored therein executable commands for execution on a processor, the processor when executing the commands performing: determining a modified Stockwell spectrum by integrating local spectra along a radial direction.

19. A system for segmenting texture of multi-dimensional data indicative of a characteristic of an object comprising: an input port for receiving the multi-dimensional data; a processor in communication with the input port for: transforming the multi-dimensional data into second multi-dimensional data within a Stockwell domain using a polar S-transform of the multi-dimensional data; applying principal component analysis to the second multi-dimensional data for generating texture data characterizing texture around each data point of at least a portion of the multi-dimensional data; and, partitioning the data points of the at least a portion of the multi-dimensional data into clusters based on the texture data using a classification process; producing a texture map based on the partitioned data points of the multi-dimensional data, wherein data points of the multi-dimensional data within a cluster corresponding to a same texture region have a same texture value assigned thereto; and, an output port in communication with the processor for providing data indicative of the texture map.

20. A system for segmenting texture as defined in claim 19 wherein the processor comprises electronic circuitry designed for performing at least a portion of transforming the signal data into second signal data and processing the second signal data.

21. A system for segmenting texture as defined in claim 19 comprising a control port in communication with the processor for receiving control commands for controlling at least one of determining a region of interest, generation of texture data, and classification.

22. A system for segmenting texture as defined in claim 21 comprising a graphical display in communication with the processor for displaying at least the data indicative of the texture map in a graphical fashion.

23. A system for segmenting texture as defined in claim 22 wherein the graphical display comprises a graphical user interface.

24. A method for segmenting texture of multi-dimensional data indicative of a characteristic of an object comprising: receiving the multi-dimensional data; transforming the multi-dimensional data into second multi-dimensional data within a space-frequency domain using a rotation-invariant localized space-frequency transformation of the multi-dimensional data; applying principal component analysis to the second multi-dimensional data for generating texture data characterizing texture around data points of at least a portion of the multi-dimensional data; and, partitioning the data points of the at least a portion of the multi-dimensional data into clusters based on the texture data using a classification process.